According to Kemelmacher et al. [1]}, recent identification and verification face recognition approaches are not very promising when the gallery set is upscaled, that is, when the set of known individuals escalates towards thousands or millions of subjects.
For that reason, scalability is drawing the attention of several researchers [2]}, [3]}, [4]}, [5]}.
Vareto et al. [6]} concentrate on the open-set face recognition problem as they assess both Partial Least Squares (PLS) and Multi-Layer Perceptron (MLP) classification models in pursuance of an algorithm that is not directly dependent on the gallery set size.
In fact, the authors build a collection of either PLS or MLP binary models, defined as hashing functions, and a voting system scheme (candidate list) to determine whether the queried subject is known or unknown.
If a subject stands out from the other individuals of the candidate list, the subject is recognized; otherwise, the subject is discarded since the method considers s/he is not enrolled in the gallery set.
